1. Field
Certain aspects of the present disclosure generally relate to neural system engineering and, more particularly, to a method and apparatus for modeling neural resource-based synaptic plasticity.
2. Background
The resource model represents a model of synaptic plasticity (neural learning) that accurately accounts for biological experimental data and allows fast, stable and diverse learning of temporal patterns in biologically consistent spiking neural networks. Thus, there is a substantial motivation to use the resource model in hardware-based or hardware-accelerated neural network simulations. It is also desirable to limit (in hardware) the frequency and amount of information being conveyed among modules or units for parallelization. The problem is how to efficiently design the resource model of synaptic plasticity.